删除深度卷积网络的全连接层中的对象选择单元可提高分类性能
Deleting object selective units in a fully-connected layer of deep convolutional networks improves classification performance
摘要：
灵长类视皮层神经元表现出广泛的刺激选择性。有些神经元只对一小部分刺激图像作出反应，而另一些神经元则以非选择性的方式对许多刺激图像作出反应。目前还不清楚刺激选择性和非选择性神经元如何促进视觉物体识别。在本文中，我们使用两种类型的深度卷积神经网络（DCNN）的完全连接层来检查刺激选择性与删除单元对任务性能的影响之间的关系。删除刺激选择性单位的任务性能略有改善，而删除刺激非选择性单位的任务性能造成了显着下降。然而，这些发现并不意味着刺激选择单位没有使用的任务。事实上，更好的性能时，网络包括刺激选择性和非选择性的单位。
英文摘要：
Neurons in the primate visual cortices show a wide range of stimulus selectivity. Some neurons respond to only a small fraction of stimulus images, whereas others respond to many stimulus images in a non-selective manner. It is unclear how stimulus selective and non-selective neurons contribute to visual object recognition. Herein, we examined the relationship between stimulus selectivity and the effect of deletion of units on task performance using fully a connected layer of two types of deep convolutional neural networks (DCNNs). Deleting a stimulus selective unit caused slight improvements of task performance, whereas deleting stimulus non-selective units caused a significant decrease in task performance. However, these findings do not imply that stimulus selective units have no use for the task. Indeed, better performance was obtained when the networks consisted of both stimulus selective and non-selective units.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
